Every Business Analytics consultant should watch the movie Moneyball (2011). This movie is indeed one of the best movies I’ve watched for a long time that showcases the application of analytics and statistics. Here’s something about the plot summary that IMDB has to say about Moneyball.
Every Business Analytics consultant should watch the movie Moneyball (2011). This movie is indeed one of the best movies I’ve watched for a long time that showcases the application of analytics and statistics. Here’s something about the plot summary that IMDB has to say about Moneyball.
“Oakland A’s GM Billy Beane is handicapped with the lowest salary constraint in baseball. If he ever wants to win the World Series, Billy must find a competitive advantage. Billy is about to turn baseball on its ear when he uses statistical data to analyze and place value on the players he picks for the team.”
What happens next is history. Oakland A’s would remember 2002, for having set an American League record of winning 20 consecutive games between August 13 – September 4, 2002. The movie ends with Bill Beane passing an opportunity to become the general manager of the Boston Red Sox’s, despite an offer of $12.5 M, which would have made him the highest paid general manager in the baseball history. What’s noteworthy is the Red Sox’s win the World Series in 2004, using the same theories that Beane pioneered. Moneyball surely has made a huge impact on baseball as the term itself has entered the lexicon of baseball.
A fairly recent post on www.b-eye-network.com too publicizes the usage of analytics in the NFL world. The most famous of those stories is the story of the Patriots and their usage of analytics in drafting their team and making winning plays on field, based on who is their opponent, where they play and what the conditions are on a particular day. Every sport does use analytics in some way or the other to gain a competitive edge over their opponents. One no longer can just rely on skill, spirit and gut instinct to win games these days, every coach and manager does need more than that to make winning plays.
This made me think the areas where the Moneyball can be best used in evaluating the resources one has and creating a winning team, not just by relying on reputation, experience and gut feel but using analytics to support your decision.
Moneyball in Recruitment
Ask any well qualified recruitment agency, what their next steps would be, had they to build a team from scratch to deliver a high stakes mammoth project. One would not be surprised, by the approach they might take out here, even in today’s world. They still would go about by checking out what the requirements are (Roles, Responsibilities, and Level of experience) and go about searching for individuals in the open market to fill up those needs.
I have not yet seen a Moneyball approach being implemented in the world of recruitment, where the agency would consider all the different factors required to make the project a success (end goal) and come out with a team, that’s best suited to deliver the project. Some might argue that using the principle of Moneyball is just not possible out here. The data that is available in the world of Sports is much refined, authentic and trustworthy than the data available in the recruitment world. Every game is recorded and the results are pretty much out there for every coach to analyze. Strengths and weaknesses of each player are available at the fingertips of every team manager. User profiles, their current and historical performance records, and in most cases even their personal information is easily available.
Is the same true in the world of IT recruitment? Resumes are like web 2.0, having completely user generated content and you have no option but to trust it. Is there any way to validate this data for its authenticity? References, background checks and interviews evaluate only technical/inter-personal strengths. How useful are their results to get an answer like “Will this consultant gel well in a team and perform exceptionally well in a high stakes stressful project making it a big success?
Does publicly available data, combined with internal, external factors throw a glimmer of hope here? Yes it could! Check out a high level vision one of the use-cases we internally are currently working on:
Is there a way an engine consumes all of the information available in the social media world, interview results, background checks, and resume information. Processes the output with the success factors required for a successful engagement and the different customer dynamics to come out with a winning (MONEYBALL) team?
Moneyball in Life Sciences
The application areas here are immense. Let me cite an example of one of a recent use-case on Key Opinion Leader identification system. Key Opinion Leaders (KOLs) are the most influential doctors who are primary targets of many Pharmaceutical companies. Such companies invite KOLs to conferences and speaking events to network with other KOLs and discuss about their drugs. The biggest challenge here is to identify the most influential KOLs based on a number of internal/external parameters, performance and their overall influence map in the Pharmaceutical world. Extensive data is procured about these KOLs both from internal systems and external data providers. Extensive reporting solutions are implemented using this data to generate lists of KOLs catering to very specific needs that an event or conference has.
The process used by such Pharmaceutical companies to shortlist KOLs surely can use the underlying algorithms or statistical analysis process that Moneyball approach utilizes. A lot of companies still consider this as a BI problem and try to solve it using traditional BI techniques. This surely is much more than just another BI problem.
What can we learn here?
There is a lot to learn from the above examples. Every organization or business who wants to gain significant competitive advantage should fully exploit the potential of analytics. To gain an edge over your competition in any field, one should not rely only on the parameters such as experience and instinct. One needs to use analytics to evaluate available resources and best ways to implement strategies for a big win. Does the above guarantee a win? No, but it surely does increase the probability for a win.
Lastly, for those who have not yet watched the Moneyball, go watch it. If you are someone who is new to Business Intelligence and Analytics, this movie might surely influence you to take up Analytics as your career.